{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction\n",
    "The purpose of this notebook is to explore the miliary data from data.world. I'm mostly interested in the size of the military relative to the country, or active members per capita."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-24T00:46:28.684451Z",
     "start_time": "2019-03-24T00:46:28.396905Z"
    }
   },
   "outputs": [],
   "source": [
    "import plotly_express as px\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-24T00:51:56.998222Z",
     "start_time": "2019-03-24T00:51:56.345951Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 172 entries, 0 to 171\n",
      "Data columns (total 8 columns):\n",
      "Country                     172 non-null object\n",
      "Active military             171 non-null object\n",
      "Reserve military            171 non-null object\n",
      "Paramilitary                171 non-null object\n",
      "Total                       172 non-null object\n",
      "Per 1000 capita (total)     172 non-null float64\n",
      "Per 1000 capita (active)    171 non-null float64\n",
      "Unnamed: 7                  0 non-null float64\n",
      "dtypes: float64(3), object(5)\n",
      "memory usage: 10.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('C:/Users/willk/Downloads/Military Data .csv')\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-24T00:52:00.496853Z",
     "start_time": "2019-03-24T00:51:59.621195Z"
    }
   },
   "outputs": [
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       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>active</th>\n",
       "      <th>reserve</th>\n",
       "      <th>para</th>\n",
       "      <th>total</th>\n",
       "      <th>total_per_1000</th>\n",
       "      <th>active_per_1000</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>171,200</td>\n",
       "      <td>0</td>\n",
       "      <td>148,200</td>\n",
       "      <td>319,400</td>\n",
       "      <td>9.6</td>\n",
       "      <td>5.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>8,000</td>\n",
       "      <td>0</td>\n",
       "      <td>500</td>\n",
       "      <td>8,500</td>\n",
       "      <td>2.8</td>\n",
       "      <td>2.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>130,000</td>\n",
       "      <td>150,000</td>\n",
       "      <td>187,200</td>\n",
       "      <td>467,200</td>\n",
       "      <td>11.6</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Angola</td>\n",
       "      <td>107,000</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>180</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>260</td>\n",
       "      <td>2.8</td>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               country   active  reserve     para    total  total_per_1000  \\\n",
       "0          Afghanistan  171,200        0  148,200  319,400             9.6   \n",
       "1              Albania    8,000        0      500    8,500             2.8   \n",
       "2              Algeria  130,000  150,000  187,200  467,200            11.6   \n",
       "3               Angola  107,000        0   10,000  117,000             5.8   \n",
       "4  Antigua and Barbuda      180       80        0      260             2.8   \n",
       "\n",
       "   active_per_1000  \n",
       "0              5.1  \n",
       "1              2.6  \n",
       "2              3.2  \n",
       "3              5.3  \n",
       "4              1.9  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.columns = ['country', 'active', 'reserve', 'para', 'total',\n",
    " 'total_per_1000', 'active_per_1000', 'unknown']\n",
    "df = df.drop(columns=['unknown'])\n",
    "df.to_csv('military_data.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
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    "ExecuteTime": {
     "end_time": "2019-03-24T00:52:24.997728Z",
     "start_time": "2019-03-24T00:52:24.259701Z"
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     "start_time": "2019-03-24T00:52:18.431391Z"
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\"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"grid\": {\"xaxes\": [\"x\"], \"yaxes\": [\"y\"], \"xgap\": 0.1, \"ygap\": 0.1, \"xside\": \"bottom\", \"yside\": \"left\"}, \"xaxis\": {\"title\": {\"text\": \"total\"}}, \"yaxis\": {\"title\": {\"text\": \"total_per_1000\"}}}, {\"showLink\": false, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\"}); \n",
       "}\n",
       "});</script><script type=\"text/javascript\">window.addEventListener(\"resize\", function(){if (document.getElementById(\"1a0f66fa-20ea-45ad-b5c8-045ae52bb347\")) {window._Plotly.Plots.resize(document.getElementById(\"1a0f66fa-20ea-45ad-b5c8-045ae52bb347\"));};})</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "px.scatter(df, 'total', 'total_per_1000', hover_name='country')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "px.scatter_geo()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns = ['country', 'active', ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "px.scatter(df, 'Active military', 'Per 1000 capita (active)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imports\n",
    "Import libraries and write settings here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-24T00:46:32.371897Z",
     "start_time": "2019-03-24T00:46:32.152483Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script><script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window._Plotly) {require(['plotly'],function(plotly) {window._Plotly=plotly;});}</script>"
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     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "# Data manipulation\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Options for pandas\n",
    "pd.options.display.max_columns = 50\n",
    "pd.options.display.max_rows = 30\n",
    "\n",
    "# Display all cell outputs\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = 'all'\n",
    "\n",
    "from IPython import get_ipython\n",
    "ipython = get_ipython()\n",
    "\n",
    "# autoreload extension\n",
    "if 'autoreload' not in ipython.extension_manager.loaded:\n",
    "    %load_ext autoreload\n",
    "\n",
    "%autoreload 2\n",
    "\n",
    "# Visualizations\n",
    "import plotly.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "from plotly.offline import iplot, init_notebook_mode\n",
    "init_notebook_mode(connected=True)\n",
    "\n",
    "import cufflinks as cf\n",
    "cf.go_offline(connected=True)\n",
    "cf.set_config_file(theme='white')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis/Modeling\n",
    "Do work here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Results\n",
    "Show graphs and stats here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusions and Next Steps\n",
    "Summarize findings here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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